Instructions to use Enayat/ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enayat/ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enayat/ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Enayat/ViT") model = AutoModelForImageClassification.from_pretrained("Enayat/ViT") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Enayat/ViT")
model = AutoModelForImageClassification.from_pretrained("Enayat/ViT")Quick Links
ViT
Created a Simple Image Classifier using ViT Transformers, custom trained on blur-dataset with an acc of about 93%. The Dataset link : https://www.kaggle.com/datasets/kwentar/blur-dataset
Architecture
Example Images
defocused_blurred
motion_blurred
sharp
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Evaluation results
- Accuracyself-reported0.930

# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enayat/ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")